






'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline












'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCAmelCase        =							False




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 12







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 12







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 32







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			VQModel(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
         return model







  @property
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
         return tokenizer







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         return CLIPTextModel(UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)

         a_						     =			12
         a_						     =			12

         a_						     =			{
             'attention_bias': True,
             'cross_attention_dim': 32,
             'attention_head_dim': height * width,
             'num_attention_heads': 1,
             'num_vector_embeds': self.num_embed,
             'num_embeds_ada_norm': self.num_embeds_ada_norm,
             'norm_num_groups': 32,
             'sample_size': width,
             'activation_fn': 'geglu-approximate',
         }

         a_						     =			TransformeraDModel(**UpperCamelCase__	)
         return model







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'

         a_						     =			self.dummy_vqvae
         a_						     =			self.dummy_text_encoder
         a_						     =			self.dummy_tokenizer
         a_						     =			self.dummy_transformer
         a_						     =			VQDiffusionScheduler(self.num_embed	)
         a_						     =			LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase__	)

         a_						     =			VQDiffusionPipeline(
             vqvae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	transformer=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	learned_classifier_free_sampling_embeddings=UpperCamelCase__   ,	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'teddy bear playing in the pool'

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe([prompt]   ,	generator=UpperCamelCase__   ,	num_inference_steps=2   ,	output_type='np'	)
         a_						     =			output.images

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	return_dict=UpperCamelCase__   ,	num_inference_steps=2	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         a_						     =			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'

         a_						     =			self.dummy_vqvae
         a_						     =			self.dummy_text_encoder
         a_						     =			self.dummy_tokenizer
         a_						     =			self.dummy_transformer
         a_						     =			VQDiffusionScheduler(self.num_embed	)
         a_						     =			LearnedClassifierFreeSamplingEmbeddings(
             learnable=UpperCamelCase__   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length	)

         a_						     =			VQDiffusionPipeline(
             vqvae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	transformer=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	learned_classifier_free_sampling_embeddings=UpperCamelCase__   ,	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'teddy bear playing in the pool'

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe([prompt]   ,	generator=UpperCamelCase__   ,	num_inference_steps=2   ,	output_type='np'	)
         a_						     =			output.images

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	return_dict=UpperCamelCase__   ,	num_inference_steps=2	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         a_						     =			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'	)

         a_						     =			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'	)
         a_						     =			pipeline.to(UpperCamelCase__	)
         pipeline.set_progress_bar_config(disable=UpperCamelCase__	)

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipeline(
             'teddy bear playing in the pool'   ,	num_images_per_prompt=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)

         a_						     =			output.images[0]

         assert image.shape == (256, 256, 3)
         assert np.abs(expected_image - image	).max() < 2.0




